Journal of Hepatocellular Carcinoma最新文献

{"title":"Atezolizumab Plus Bevacizumab in Patients with Unresectable Hepatocellular Carcinoma: Real-World Experience From a US Community Oncology Network.","authors":"David Cosgrove, Ruoding Tan, Andrew J Osterland, Sairy Hernandez, Sarika Ogale, Sami Mahrus, John Murphy, Thomas Wilson, Gregory Patton, Arturo Loaiza-Bonilla, Amit G Singal","doi":"10.2147/JHC.S492881","DOIUrl":"https://doi.org/10.2147/JHC.S492881","url":null,"abstract":"<p><strong>Purpose: </strong>Atezolizumab plus bevacizumab (atezo-bev) is a preferred first-line (1L) systemic therapy option for unresectable hepatocellular carcinoma (uHCC). However, evidence of its effectiveness in real-world clinical practice, including in patients with impaired liver function, remains limited.</p><p><strong>Patients and methods: </strong>This retrospective observational study included adult patients who initiated 1L atezo-bev for uHCC within The US Oncology Network between 1/1/2019 and 8/31/2022 using structured and unstructured electronic health records data. Overall survival (OS) and real-world progression-free survival (rwPFS) were assessed using Kaplan-Meier methods for the overall cohort and in a subgroup of \"trial-like\" patients with characteristics that were consistent with those of the IMbrave150 Trial (ECOG performance status 0-1, Child-Pugh class A, albumin-bilirubin grade 1-2).</p><p><strong>Results: </strong>Overall, 374 patients met eligibility criteria (mean age 68.8 years, 78.9% male, 31% Child-Pugh class B-C among reported, 18% ECOG performance status ≥2 among reported), of whom 132 patients comprised the trial-like subgroup. At a median follow-up of 5.6 months, median (95% CI) OS was 13.2 (9.5, 15.9) months and rwPFS was 6.4 (5.1, 7.7) months. In the trial-like subgroup, median (95% CI) OS was 16.5 (13.2, NR) months and rwPFS was 9.4 (5.7, 12.5) months.</p><p><strong>Conclusion: </strong>Atezo-bev was used as 1L systemic therapy for HCC in a diverse patient population across US community oncology settings. Real-world effectiveness of atezo-bev among trial-like patients is comparable to that reported in the Phase 3 study. These data can help guide selection of appropriate treatment candidates and maximize the benefits of atezo-bev in routine clinical practice.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"791-804"},"PeriodicalIF":4.2,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12015733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144004672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Prognostic Signature Integrating Immune and Glycolytic Pathways for Enhanced Prognosis and Immunotherapy Prediction in Hepatocellular Carcinoma.","authors":"Zeyu Zhang, Hongxi Zhao, Pengyu Wang, Xueyan Geng, Maopeng Yin, Yingjie Liu, Shoucai Zhang, Yongyuan Liang, Jian Ji, Guixi Zheng","doi":"10.2147/JHC.S510460","DOIUrl":"https://doi.org/10.2147/JHC.S510460","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to establish an immune-glycolysis-related prognostic signature (IGRPS) to predict hepatocellular carcinoma (HCC) outcomes. Additionally, it explored the role of this signature in the tumor immune microenvironment (TIME), glycolytic pathways, and immunotherapy.</p><p><strong>Methods: </strong>We analyzed RNA-seq, single-cell sequencing, and immune- and glycolysis-related gene datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Using weighted gene co-expression network analysis (WGCNA), F-test, and Cox regression, we identified key survival-related immune and glycolytic genes (SRIGRGs) and developed an IGRPS through multivariate Cox regression. The IGRPS's predictive performance was validated in training and validation cohorts using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and a prognostic nomogram. Its correlation with TIME and its ability to predict immunotherapy outcomes were also assessed. In vitro experiments were conducted to analyze the expression and function of IGRPS genes in HCC.</p><p><strong>Results: </strong>Thirteen SRIGRGs were identified for constructing the IGRPS. Patients with low-risk scores had significantly longer survival times. The area under the curve (AUC) for ROC curves was over 0.73 for training and 0.7 for validation cohorts, with C-indices of 0.721 and 0.79, respectively. IGRPS was confirmed as an independent prognostic indicator. Patients in the low-risk group showed better responses to combined anti-CTLA4 and anti-PD-1 therapies. In vitro experiments indicated that PRKAG1 and B3GAT3 were upregulated, enhancing glycolysis and promoting HCC cell proliferation and migration.</p><p><strong>Conclusion: </strong>The IGRPS, based on immune- and glycolysis-related genes, effectively predicted prognosis and immunotherapy responses in HCC patients.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"805-823"},"PeriodicalIF":4.2,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12013648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144024337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Nomogram Model for Predicting the Risk of Hepatocellular Carcinoma in Patients with Chronic Hepatitis B Infection.","authors":"Yanfang Wu, Meixia Wang, Zhenzhen Zhang, Guobin Chen, Boheng Zhang","doi":"10.2147/JHC.S512471","DOIUrl":"https://doi.org/10.2147/JHC.S512471","url":null,"abstract":"<p><strong>Purpose: </strong>Hepatitis B virus (HBV) infection is a major cause of hepatocellular carcinoma (HCC). This study aimed to construct a novel nomogram model for predicting the risk of HCC in patients with HBV infection.</p><p><strong>Patients and methods: </strong>This retrospective study analyzed clinical data from healthcare databases in Xiamen, encompassing 5161 adults with HBV infection without HCC and 2819 adults with HBV-related HCC between January 2016 and December 2020. Subsequently, the patients were randomly divided into a training set (n=5586) and testing set (n=2394). The training set was used to identify the risk factors for HCC development and to construct an HCC risk prediction nomogram model. The predictive accuracy of the model was assessed using the receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) in both sets. Furthermore, the performance of the nomogram model was compared with that of the existing models.</p><p><strong>Results: </strong>Multivariate analysis revealed that age, sex, liver cirrhosis, neutrophil/platelet count ratio (NLR), serum bilirubin (TBIL), aspartate aminotransferase (AST), serum albumin (ALB), serum alpha-fetoprotein (AFP), and HBV DNA were independently associated with HCC. A nomogram model was developed by incorporating these risk factors. The the receiver operating characteristic curve (AUC) of the nomogram model were 0.897 and 0.902 for the training and testing sets, respectively. Analysis of the AUC demonstrated that the nomogram model exhibited significantly enhanced predictive performance for HCC compared to the alternative risk scores in both sets. Furthermore, DCA indicated that the nomogram model provided a broad range of threshold probabilities related to the net clinical benefits. A web-based calculator was developed(https://nomogram-model-hcc.shinyapps.io/DynNomapp/).</p><p><strong>Conclusion: </strong>The novel nomogram model, which includes age, sex, liver cirrhosis, NLR, TBIL, AST, ALB, AFP, and HBV DNA as factors, precisely predicts the risk of HCC in patients with chronic hepatitis B(CHB) and outperforms the existing models.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"765-775"},"PeriodicalIF":4.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12009588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144024973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Treatment Selection for Early Hepatocellular Carcinoma Based on Tumor Biology, Liver Function, and Patient Status.","authors":"Xing Li, Yong Xu, Yanmei Ou, Huikai Li, Wengui Xu","doi":"10.2147/JHC.S514248","DOIUrl":"https://doi.org/10.2147/JHC.S514248","url":null,"abstract":"<p><p>Early-stage hepatocellular carcinoma (HCC) represents a critical window for curative treatment. However, treatment selection is complicated by significant heterogeneity in tumor biology, liver function, and patient performance status. This review provides a comprehensive overview of current curative-intent strategies for early-stage HCC, including liver transplantation, surgical resection, and local ablative therapies. We emphasize the importance of integrating tumor-specific characteristics-such as microvascular invasion, size, and anatomical location-with liver reserve metrics, including portal hypertension, Child-Pugh classification, and novel indices like albumin-bilirubin and albumin-indocyanine green evaluation grades. Furthermore, we discuss recent advances in non-thermal ablation techniques (eg, high-intensity focused ultrasound and irreversible electroporation), and technical innovations in radiofrequency ablation and cryoablation that are expanding the therapeutic landscape. By combining macro-level functional assessments with micro-level biological indicators, this review advocates for a personalized, evidence-based framework to optimize long-term outcomes in early HCC. The future of HCC management lies in standardizing individualized therapy.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"777-790"},"PeriodicalIF":4.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12009567/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144007390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic Analysis of Elderly Patients with Hepatocellular Carcinoma: an Exploration and Machine Learning Model Prediction Based on Age Stratification and Surgical Approach.","authors":"Chiyu Cai, Hengli Zhu, Bingyao Li, Changqian Tang, Yongnian Ren, Yuqi Guo, Jizhen Li, Liancai Wang, Deyu Li, Dongxiao Li","doi":"10.2147/JHC.S512410","DOIUrl":"https://doi.org/10.2147/JHC.S512410","url":null,"abstract":"<p><strong>Purpose: </strong>As the global population ages, precise prognostic tools are needed to optimize postoperative care for elderly hepatocellular carcinoma (HCC) patients. This study established a machine learning-driven predictive model to identify key prognostic determinants and evaluate age/surgical approach impacts, overcoming limitations of traditional statistical methods.</p><p><strong>Methods: </strong>This retrospective study included 252 postoperative HCC patients aged ≥65 years (mean age 69.0±4.3; 68.25% male). Patients were randomly divided into training (70%, n=177) and validation sets (30%, n=75). We evaluated 147 machine learning models to establish the optimal predictive model. Patients were grouped by age (>75 vs ≤75 years) and surgical approach (laparoscopic vs open).</p><p><strong>Results: </strong>The LASSO+RSF model showed strong predictive performance with AUC values of 0.869 and 0.818 in the training and validation sets, respectively. Time-dependent AUCs for 1-, 2- and 3-year survival were 0.874, 0.903, and 0.883 in the training set, and 0.878, 0.882, and 0.915 in the validation set. Key predictors included age-adjusted Charlson index (ACCI, LASSO+RSF synergistic weight (LRSW) =0.160), microvascular invasion (0.111), tumor capsule integrity (0.034), and lymphatic invasion (0.023), while three variables (intraoperative blood loss, tumor margin, WBC) were excluded (LRSW<0.01). A web-based dynamic nomogram (https://cliniometrics.shinyapps.io/LRSF-GeroHCC/) enabled real-time risk stratification. Patients >75 years had longer length of stay (16 vs 14 days, <i>P</i>=0.033), higher Clavien-Dindo scores (<i>P</i>=0.014), higher ACCI scores (5.5 vs 4.0, <i>P</i>=0.002), and lower PFS (16.5 vs 24 months, <i>P</i>=0.041). Laparoscopic surgery was associated with longer operative time (202.5 vs 159.0min, <i>P</i><0.001), shorter length of stay (14 vs 17days, <i>P</i><0.001), and lower Clavien-Dindo scores (<i>P</i>=0.038).</p><p><strong>Conclusion: </strong>The LASSO+RSF model provides validated tools for personalized prognosis management in elderly HCC patients, emphasizing age-adapted surgical strategies and comorbidity-focused perioperative care.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"747-764"},"PeriodicalIF":4.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143969604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Serum Osteopontin Enhances Hepatocellular Carcinoma Diagnosis and Predicts Anti-PD-L1 Immunotherapy Benefit.","authors":"Miantao Wu, Fei Zou, Suyin He, Yingqi Pi, Yiling Song, Shulin Chen, Linfang Li","doi":"10.2147/JHC.S514144","DOIUrl":"https://doi.org/10.2147/JHC.S514144","url":null,"abstract":"<p><strong>Background: </strong>Osteopontin (OPN), a phosphorylated glycoprotein encoded by SPP1, critical in hepatic inflammation and fibrosis, requires further investigation for its role on hepatocellular carcinoma (HCC) and predictive value for anti-programmed cell death ligand 1 (anti-PD-L1) immunotherapy responses.</p><p><strong>Methods: </strong>Publicly available datasets were utilized to explore OPN expression in HCC. A retrospective cohort study involving 316 participants, recruited from January 2015 to March 2017. Serum OPN levels were measured by enzyme-linked immunosorbent assay. Diagnostic performance was assessed using receiver operating characteristic (ROC) curves, a logistic regression model was developed for early HCC diagnosis. Prospective follow-up was conducted from 2017 to 2024 to evaluate overall survival (OS) and disease-free survival (DFS) using Kaplan-Meier analyses. The survival benefit of anti-PD-L1 immunotherapy for patients with OPN patterns was investigated.</p><p><strong>Results: </strong>Serum OPN levels were significantly elevated in HCC compared to chronic liver disease and healthy individuals (both <i>p</i> <0.001). The area under the curve (AUC) for OPN was 0.903, with 88.2% sensitivity and 83.3% specificity, significantly superior to AFP alone (AUC: 0.707). A combined diagnostic model integrating OPN with alpha-fetoprotein (AFP) and aspartate aminotransferase (AST) enhanced accuracy further (AUC: 0.941). High OPN levels indicated higher tumor burden and predicted worse clinical outcomes (mean OS: 49.1 vs 75.1 months; mean DFS: 37.7 vs 60.9 months, respectively; both log-rank <i>p</i> <0.001). Anti-PD-L1 immunotherapy significantly prolonged survival (OS: 62.9 vs 38.0 months, p = 0.009; DFS: 48.7 vs 28.6 months, p = 0.033) in patients with OPN high pattern.</p><p><strong>Conclusion: </strong>Serum OPN demonstrates standalone diagnostic value for HCC and enhances conventional biomarker panels when combined with AFP and AST. OPN high pattern identify patients likely to benefit from anti-PD-L1 immunotherapy, suggesting its dual utility as a diagnostic and predictive biomarker.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"729-745"},"PeriodicalIF":4.2,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preoperative Prediction of Macrotrabecular-Massive Hepatocellular Carcinoma Using Machine Learning-Based Ultrasomics.","authors":"Yahong Li, Shaobo Duan, Shanshan Ren, Dujuan Li, Yujing Ma, Didi Bu, Yuanyuan Liu, Xiaoxiao Li, Xiguo Cai, Lianzhong Zhang","doi":"10.2147/JHC.S508091","DOIUrl":"https://doi.org/10.2147/JHC.S508091","url":null,"abstract":"<p><strong>Purpose: </strong>Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) is a special pathological subtype of HCC, which is related to invasiveness and poor prognosis. We aimed to construct an ultrasomics model for preoperative noninvasive prediction of MTM-HCC.</p><p><strong>Patients and methods: </strong>Patients with pathologically confirmed HCC who underwent liver surgery between January 2021 and December 2023 were retrospectively enrolled. 211 eligible patients (169 males and 42 females) were divided 7:3 into the training set (n=147) and test set (n=64) by random stratified sampling. Ultrasomics models were constructed based on the ultrasound image features of the training set using five different ML algorithms, including random forest (RF), eXtreme gradient boosting (XGBoost), support vector machine (SVM), decision tree (DT), and logistic regression (LR). Additionally, a model based on clinical features and a combined model based on clinical and ultrasomics features were constructed to predict MTM-HCC. The performance of the models in the preoperative prediction of MTM-HCC was evaluated on the test set using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.</p><p><strong>Results: </strong>The ultrasomics models and the combined models of the five algorithms were effective in predicting MTM-HCC, and the combined models have improved AUC after adding clinical features compared with the ultrasomics model in the test set. The model constructed based on the RF algorithm in the test set has a high accuracy rate and specificity, and the overall performance of the models is better than that of the other four algorithm models, the AUC, accuracy, specificity, and sensitivity of its combined model and ultrasomics model are significantly higher than the clinical model.</p><p><strong>Conclusion: </strong>ML-based ultrasomics model is an effective tool for predicting MTM-HCC before surgery. Integrating clinical and ultrasound image features enhances predictive performance, offering a novel approach for non-invasive preoperative diagnosis of MTM-HCC.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"715-727"},"PeriodicalIF":4.2,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12002077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143999573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rare Gingival Metastasis Occurring After Conversion Therapy Followed by Resection of Initially Unresectable Hepatocellular Carcinoma: A Case Report.","authors":"Juncheng Zhan, Hongwei Huang, Tianao Zhan, Xinkang Liu, Qi Cheng","doi":"10.2147/JHC.S514983","DOIUrl":"https://doi.org/10.2147/JHC.S514983","url":null,"abstract":"<p><p>Gingival metastases from hepatocellular carcinoma (HCC) are exceedingly rare and highly prone to be misdiagnosed without biopsy. Here, we report an initially unresectable HCC patient who received effective conversion therapy but discovered gingival metastasis within one-month post-hepatectomy. A 53-year-old male with a huge liver tumor diagnosed as unresectable HCC received conversion therapy of hepatic arterial infusion chemotherapy (HAIC) combined with lenvatinib and tislelizumab. During the conversion therapy, he experienced sore gingiva which was regarded as a side effect of lenvatinib. Considering the significant shrinkage of tumor after 10-month treatment, salvage resection was conducted with negative margin and no postoperative complications. Gingival oligometastases were identified and resected half month after surgery. Throughout the 1-year follow-up period, the patient remained alive; however, there was a recurrence of the gingival metastasis at the same site six months postoperatively. Hence, clinicians should regard gingival swelling and pain not merely as potential adverse events of conversion therapy but also as potential indicators of gingival metastasis.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"705-713"},"PeriodicalIF":4.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11994111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144012761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of FDFT1 and PGRMC1 as New Biomarkers in Nonalcoholic Steatohepatitis (NASH)-Related Hepatocellular Carcinoma by Deep Learning.","authors":"Qiqi Liu, Yinuo Yang, Yongshuai Wang, Shuhang Wei, Liu Yang, Tiantian Liu, Zhen Yu, Yuemin Feng, Ping Yao, Qiang Zhu","doi":"10.2147/JHC.S505752","DOIUrl":"https://doi.org/10.2147/JHC.S505752","url":null,"abstract":"<p><strong>Background: </strong>With the global epidemic of obesity and diabetes, non-alcoholic fatty liver disease (NAFLD) is becoming the most common chronic liver disease, and NASH is increasingly becoming a major risk factor for hepatocellular carcinoma. Therefore, it is essential to explore novel biomarkers in NASH-related HCC.</p><p><strong>Methods: </strong>Deep Learning (DL) methods are a promising and encouraging tool widely used in genomics by automatically applying neural networks (NNs). Therefore, DL, \"limma package\", weighted gene co-expression network analysis (WGCNA), and Protein-Protein Interaction Networks (PPI) were used to screen feature genes. Real-time quantitative PCR was used to validate the expression of feature genes in the NAFLD mice model. Enrichment and single-cell sequencing analyses of single genes were performed to investigate the role of feature genes in NASH-related HCC.</p><p><strong>Results: </strong>Combined core genes screened by DL in NAFLD with important genes in metabolic syndrome, six feature genes (FDFT1, TNFSF10, DNAJC16, RDH11, PGRMC1, and MYC) were obtained. ROC analysis demonstrates the model's superiority with the AUC was 0.983 (0.9241-0.98885). Animal experiments based on NAFLD mouse models have also shown that FDFT1, TNFSF10, DNAJC16, RDH11, and PGRMC1 have a higher expression in NAFLD livers. Among the feature genes, FDFT1 and PGRMC1 showed significant expression trends and outstanding diagnosis value in NASH-HCC.</p><p><strong>Conclusion: </strong>In conclusion, FDFT1 and PGRMC1 are key enzymes in the cholesterol synthesis pathway, our study validates the important role of cholesterol metabolism in NAFLD from another perspective, implying they may be new prognostic and diagnostic markers for NASH-HCC.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"685-704"},"PeriodicalIF":4.2,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11980943/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143996037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ALBI Grade Enables Risk Stratification for Bleeding Events and Refines Prognostic Prediction in Advanced HCC Following Atezolizumab and Bevacizumab.","authors":"Bernardo Stefanini, Claudia Angela Maria Fulgenzi, Bernhard Scheiner, James Korolewicz, Jaekyung Cheon, Naoshi Nishida, Celina Ang, Thomas U Marron, Y Linda Wu, Anwaar Saeed, Brooke Wietharn, Lorenza Rimassa, Angelo Pirozzi, Antonella Cammarota, Tiziana Pressiani, Matthias Pinter, Lorenz Balcar, Yi-Hsiang Huang, Aman Mehan, Samuel Phen, Caterina Vivaldi, Francesca Salani, Gianluca Masi, Dominik Bettinger, Arndt Vogel, Martin Schönlein, Johann von Felden, Kornelius Schulze, Henning Wege, Adel Samson, Peter R Galle, Masatoshi Kudo, Giulia Francesca Manfredi, Ciro Celsa, Nichola Awosika, Alessio Cortellini, Amit G Singal, Rohini Sharma, Hong Jae Chon, Francesco Tovoli, Fabio Piscaglia, David James Pinato, Antonio D'Alessio","doi":"10.2147/JHC.S462701","DOIUrl":"10.2147/JHC.S462701","url":null,"abstract":"<p><strong>Background and aims: </strong>Atezolizumab and bevacizumab (A+B) are recommended for treating unresectable hepatocellular carcinoma (HCC). Although highly effective, A+B can lead to potentially life-threatening adverse events including bleeding. We investigated whether albumin-bilirubin (ALBI) grade identifies patients with a higher risk of bleeding and its impact on prognosis than the Child-Pugh (CP) score.</p><p><strong>Methods: </strong>We performed a multicenter retrospective study of 15 tertiary referral centers that consecutively treated patients with A+B. We analyzed the association between the ALBI grade and gastrointestinal bleeding using the χ2 test. Overall survival (OS) stratified by ALBI was estimated using the Kaplan-Meier method and the predictive value for the 6-months OS landmark with ROC curves.</p><p><strong>Results: </strong>Of the 368 patients included in the analysis, 163 (44.3%), 192 (52.2%) and 13 (3.5%) had ALBI 1, ALBI 2, and ALBI 3, respectively. ALBI grade was associated with a 3-fold increase in bleeding risk (3.1% in ALBI 1 vs 10.2% in ALBI 2/3, p=0.008). Among 192 patients with pre-treatment EGD, G2 and G3 varices were associated with an increased risk of bleeding, whereas G1 varices had a similar risk as no varices. Patients with ALBI 1 achieved a longer median OS (not reached; 95% CI, 24.9-33.7), than ALBI 2 (9.7 months; 95% CI, 7.0-12.3) or ALBI 3 (5.6 months; 95% CI, 0.1-12.0). ALBI outperformed the CP score for predicting 6-month OS with an AUC 0.79 of ALBI versus 0.71 for the CP score (p=0.01).</p><p><strong>Conclusion: </strong>A Higher ALBI grade was associated with an increased risk of gastrointestinal bleeding after receiving A+B, and outperformed the CP score in predicting worse survival.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"12 ","pages":"671-683"},"PeriodicalIF":4.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11977629/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143811544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}